import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def 	_UpperCamelCase (      snake_case__				)    -> Optional[Any]:





							if (
							    (cp >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
							    or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f)  #
							    or (cp >= 0X2_0_0_0_0 and cp <= 0X2_a_6_d_f)  #
							    or (cp >= 0X2_a_7_0_0 and cp <= 0X2_b_7_3_f)  #
							    or (cp >= 0X2_b_7_4_0 and cp <= 0X2_b_8_1_f)  #
							    or (cp >= 0X2_b_8_2_0 and cp <= 0X2_c_e_a_f)  #
							    or (cp >= 0Xf_9_0_0 and cp <= 0Xf_a_f_f)
							    or (cp >= 0X2_f_8_0_0 and cp <= 0X2_f_a_1_f)  #
							):  #
														return True

							return False




def 	_UpperCamelCase (      snake_case__				)    -> Optional[Any]:





							for char in word:
														__UpperCAmelCase    :      Dict       = ord(__snake_case				)
														if not _is_chinese_char(__snake_case				):
																					return 0
							return 1




def 	_UpperCamelCase (      snake_case__				)    -> List[str]:
							__UpperCAmelCase    :      Optional[int]       = set()

							for token in tokens:
														__UpperCAmelCase    :      Optional[Any]       = len(__snake_case				) > 1 and is_chinese(__snake_case				)
														if chinese_word:
																					word_set.add(__snake_case				)
							__UpperCAmelCase    :      Tuple       = list(__snake_case				)
							return word_list




def 	_UpperCamelCase (      snake_case__,     snake_case__				)    -> str:





							if not chinese_word_set:
														return bert_tokens
							__UpperCAmelCase    :      str       = max([len(__snake_case				) for w in chinese_word_set]				)

							__UpperCAmelCase    :      Optional[int]       = bert_tokens
							__UpperCAmelCase     ,						__UpperCAmelCase    :      Union[str, Any]       = 0, len(__snake_case				)
							while start < end:
														__UpperCAmelCase    :      Union[str, Any]       = True
														if is_chinese(bert_word[start]				):
																					__UpperCAmelCase    :      Any       = min(end - start,     __snake_case				)
																					for i in range(__snake_case,     1,     -1				):
																												__UpperCAmelCase    :      Optional[int]       = "".join(bert_word[start : start + i]				)
																												if whole_word in chinese_word_set:
																																			for j in range(start + 1,     start + i				):
																																										__UpperCAmelCase    :      Optional[Any]       = "##" + bert_word[j]
																																			__UpperCAmelCase    :      Tuple       = start + i
																																			__UpperCAmelCase    :      List[str]       = False
																																			break
														if single_word:
																					start += 1
							return bert_word




def 	_UpperCamelCase (      snake_case__,     snake_case__,     snake_case__				)    -> List[Any]:
							__UpperCAmelCase    :      int       = []

							for i in range(0,     len(__snake_case				),     100				):
														__UpperCAmelCase    :      Optional[Any]       = ltp_tokenizer.pipeline(lines[i : i + 100],     tasks=["cws"]				).cws
														__UpperCAmelCase    :      str       = [get_chinese_word(__snake_case				) for r in res]
														ltp_res.extend(__snake_case				)
							assert len(__snake_case				) == len(__snake_case				)

							__UpperCAmelCase    :      List[Any]       = []
							for i in range(0,     len(__snake_case				),     100				):
														__UpperCAmelCase    :      List[Any]       = bert_tokenizer(lines[i : i + 100],     add_special_tokens=__snake_case,     truncation=__snake_case,     max_length=512				)
														bert_res.extend(res["input_ids"]				)
							assert len(__snake_case				) == len(__snake_case				)

							__UpperCAmelCase    :      Dict       = []
							for input_ids, chinese_word in zip(__snake_case,     __snake_case				):
														__UpperCAmelCase    :      int       = []
														for id in input_ids:
																					__UpperCAmelCase    :      Optional[int]       = bert_tokenizer._convert_id_to_token(__snake_case				)
																					input_tokens.append(__snake_case				)
														__UpperCAmelCase    :      Tuple       = add_sub_symbol(__snake_case,     __snake_case				)
														__UpperCAmelCase    :      int       = []
														# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
														for i, token in enumerate(__snake_case				):
																					if token[:2] == "##":
																												__UpperCAmelCase    :      int       = token[2:]
																												# save chinese tokens' pos
																												if len(__snake_case				) == 1 and _is_chinese_char(ord(__snake_case				)				):
																																			ref_id.append(__snake_case				)
														ref_ids.append(__snake_case				)

							assert len(__snake_case				) == len(__snake_case				)

							return ref_ids




def 	_UpperCamelCase (      snake_case__				)    -> List[str]:





							with open(args.file_name,     "r",     encoding="utf-8"				) as f:
														__UpperCAmelCase    :      List[Any]       = f.readlines()
							__UpperCAmelCase    :      List[Any]       = [line.strip() for line in data if len(__snake_case				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
							__UpperCAmelCase    :      str       = LTP(args.ltp				)  # faster in GPU device
							__UpperCAmelCase    :      Tuple       = BertTokenizer.from_pretrained(args.bert				)

							__UpperCAmelCase    :      Union[str, Any]       = prepare_ref(__snake_case,     __snake_case,     __snake_case				)

							with open(args.save_path,     "w",     encoding="utf-8"				) as f:
														__UpperCAmelCase    :      Union[str, Any]       = [json.dumps(__snake_case				) + "\n" for ref in ref_ids]
														f.writelines(__snake_case				)


if __name__ == "__main__":
	_snake_case								=    argparse.ArgumentParser(description='''prepare_chinese_ref''')
	parser.add_argument(
	    '''--file_name''',
	    required=False,
	    type=str,
	    default='''./resources/chinese-demo.txt''',
	    help='''file need process, same as training data in lm''',
	)
	parser.add_argument(
	    '''--ltp''',
	    required=False,
	    type=str,
	    default='''./resources/ltp''',
	    help='''resources for LTP tokenizer, usually a path''',
	)
	parser.add_argument(
	    '''--bert''',
	    required=False,
	    type=str,
	    default='''./resources/robert''',
	    help='''resources for Bert tokenizer''',
	)
	parser.add_argument(
	    '''--save_path''',
	    required=False,
	    type=str,
	    default='''./resources/ref.txt''',
	    help='''path to save res''',
	)

	_snake_case								=    parser.parse_args()
	main(args)












"""simple docstring"""
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 							A						(				__snake_case: Tuple  )    ->		Optional[Any]:

       """simple docstring"""





       if (
           (cp >= 0x4_E00 and cp <= 0x9_FFF)
           or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
           or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
           or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
           or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
           or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
           or (cp >= 0xF_900 and cp <= 0xF_AFF)
           or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
       ):  #
              return True

       return False


def 							A						(				__snake_case: str  )    ->		Optional[Any]:

       """simple docstring"""





       for char in word:
              __magic_name__      				=    ord(__snake_case  )
              if not _is_chinese_char(__snake_case  ):
                     return 0
       return 1


def 							A						(				__snake_case: List[str]  )    ->		List[str]:

       """simple docstring"""





       __magic_name__      				=    set()

       for token in tokens:
              __magic_name__      				=    len(__snake_case  ) > 1 and is_chinese(__snake_case  )
              if chinese_word:
                     word_set.add(__snake_case  )
       __magic_name__      				=    list(__snake_case  )
       return word_list


def 							A						(				__snake_case: List[str] ,     __snake_case: set()  )    ->		str:

       """simple docstring"""





       if not chinese_word_set:
              return bert_tokens
       __magic_name__      				=    max([len(__snake_case  ) for w in chinese_word_set]  )

       __magic_name__      				=    bert_tokens
       __magic_name__				,  __magic_name__      				=    0, len(__snake_case  )
       while start < end:
              __magic_name__      				=    True
              if is_chinese(bert_word[start]  ):
                     __magic_name__      				=    min(end - start ,     __snake_case  )
                     for i in range(__snake_case ,     1 ,     -1  ):
                            __magic_name__      				=    ''.join(bert_word[start : start + i]  )
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1 ,     start + i  ):
                                          __magic_name__      				=    '##' + bert_word[j]
                                   __magic_name__      				=    start + i
                                   __magic_name__      				=    False
                                   break
              if single_word:
                     start += 1
       return bert_word


def 							A						(				__snake_case: List[str] ,     __snake_case: LTP ,     __snake_case: BertTokenizer  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    []

       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    ltp_tokenizer.pipeline(lines[i : i + 1_0_0] ,     tasks=['cws']  ).cws
              __magic_name__      				=    [get_chinese_word(__snake_case  ) for r in res]
              ltp_res.extend(__snake_case  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    bert_tokenizer(lines[i : i + 1_0_0] ,     add_special_tokens=__snake_case ,     truncation=__snake_case ,     max_length=5_1_2  )
              bert_res.extend(res['input_ids']  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for input_ids, chinese_word in zip(__snake_case ,     __snake_case  ):
              __magic_name__      				=    []
              for id in input_ids:
                     __magic_name__      				=    bert_tokenizer._convert_id_to_token(__snake_case  )
                     input_tokens.append(__snake_case  )
              __magic_name__      				=    add_sub_symbol(__snake_case ,     __snake_case  )
              __magic_name__      				=    []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(__snake_case  ):
                     if token[:2] == "##":
                            __magic_name__      				=    token[2:]
                            # save chinese tokens' pos
                            if len(__snake_case  ) == 1 and _is_chinese_char(ord(__snake_case  )  ):
                                   ref_id.append(__snake_case  )
              ref_ids.append(__snake_case  )

       assert len(__snake_case  ) == len(__snake_case  )

       return ref_ids


def 							A						(				__snake_case: Optional[Any]  )    ->		List[str]:

       """simple docstring"""





       with open(args.file_name ,     'r' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    f.readlines()
       __magic_name__      				=    [line.strip() for line in data if len(__snake_case  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       __magic_name__      				=    LTP(args.ltp  )  # faster in GPU device
       __magic_name__      				=    BertTokenizer.from_pretrained(args.bert  )

       __magic_name__      				=    prepare_ref(__snake_case ,     __snake_case ,     __snake_case  )

       with open(args.save_path ,     'w' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    [json.dumps(__snake_case  ) + '\n' for ref in ref_ids]
              f.writelines(__snake_case  )


if __name__ == "__main__":
 snake_case : Tuple     		=		argparse.ArgumentParser(description="""prepare_chinese_ref""")
 parser.add_argument(
     """--file_name""",
     required=False,
     type=str,
     default="""./resources/chinese-demo.txt""",
     help="""file need process, same as training data in lm""",
 )
 parser.add_argument(
     """--ltp""",
     required=False,
     type=str,
     default="""./resources/ltp""",
     help="""resources for LTP tokenizer, usually a path""",
 )
 parser.add_argument(
     """--bert""",
     required=False,
     type=str,
     default="""./resources/robert""",
     help="""resources for Bert tokenizer""",
 )
 parser.add_argument(
     """--save_path""",
     required=False,
     type=str,
     default="""./resources/ref.txt""",
     help="""path to save res""",
 )

 snake_case : Any     		=		parser.parse_args()
 main(args)